diff --git a/JenkinsFIleEval b/JenkinsFIleEval
new file mode 100644
index 0000000..e69de29
diff --git a/evaluate_network.py b/evaluate_network.py
new file mode 100644
index 0000000..52421ab
--- /dev/null
+++ b/evaluate_network.py
@@ -0,0 +1,38 @@
+import pandas as pd
+import numpy as np
+from sklearn.metrics import mean_squared_error
+from tensorflow import keras
+
+model = keras.models.load_model('model')
+data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
+                   skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
+                                                                        "publish_date", "publish_hour", "category_id",
+                                                                        "channel_title", "views", "likes", "dislikes",
+                                                                        "comment_count"]).dropna()
+X_test = data.loc[:,data.columns == "views"].astype(int)
+y_test = data.loc[:,data.columns == "likes"].astype(int)
+
+min_val_sub = np.min(X_test)
+max_val_sub = np.max(X_test)
+X_test = (X_test - min_val_sub) / (max_val_sub - min_val_sub)
+print(min_val_sub)
+print(max_val_sub)
+
+min_val_like = np.min(y_test)
+max_val_like = np.max(y_test)
+print(min_val_like)
+print(max_val_like)
+
+prediction = model.predict(X_test)
+
+prediction_denormalized = []
+for pred in prediction:
+    denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
+    prediction_denormalized.append(denorm)
+
+f = open("predictions.txt", "w")
+for (pred, test) in zip(prediction_denormalized, y_test.values):
+    f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))
+
+error = mean_squared_error(y_test, prediction_denormalized)
+print(error)
diff --git a/neural_network.py b/neural_network.py
index d3ffe28..aa6f4a4 100644
--- a/neural_network.py
+++ b/neural_network.py
@@ -42,37 +42,4 @@ model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absol
 
 model.fit(X, y, epochs=int(sys.argv[1]), validation_split = 0.3)
 
-data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
-                   skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
-                                                                        "publish_date", "publish_hour", "category_id",
-                                                                        "channel_title", "views", "likes", "dislikes",
-                                                                        "comment_count"]).dropna()
-X_test = data.loc[:,data.columns == "views"].astype(int)
-y_test = data.loc[:,data.columns == "likes"].astype(int)
-
-min_val_sub = np.min(X_test)
-max_val_sub = np.max(X_test)
-X_test = (X_test - min_val_sub) / (max_val_sub - min_val_sub)
-print(min_val_sub)
-print(max_val_sub)
-
-min_val_like = np.min(y_test)
-max_val_like = np.max(y_test)
-print(min_val_like)
-print(max_val_like)
-
-prediction = model.predict(X_test)
-
-prediction_denormalized = []
-for pred in prediction:
-    denorm = pred[0] * (max_val_like[0] - min_val_like[0]) + min_val_like[0]
-    prediction_denormalized.append(denorm)
-
-f = open("predictions.txt", "w")
-for (pred, test) in zip(prediction_denormalized, y_test.values):
-    f.write("predicted: %s expected: %s\n" % (str(pred), str(test[0])))
-
-error = mean_squared_error(y_test, prediction_denormalized)
-print(error)
-
 model.save('model')
\ No newline at end of file